PONE-D-20-30514

Learning epistatic gene interactions from perturbation screens

PLOS ONE

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[This work has partially been funded by SystemsX.ch, the Swiss Initiative in Systems Biology,under IPhD grant 2009/025 and RTD grants 51RT-0126008 (InfectX) and 51RTP0151029(TargetInfectX), evaluated by the Swiss National Science Foundation.We acknowledge support from the Royal Society Te Ap ̄arangi through a Rutherford DiscoveryFellowship (RDF-UOO1702) awarded to AG. This work was partially supported by Ministryof Business, Innovation, and Employment of New Zealand through an Endeavour Smart Ideasgrant (UOOX1912) and a Data Science Programmes grant (UOAX1932).]

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper describes a technique for inferring epistatic interactions from siRNA perturbation screens. The main ideas are (1) to treat the off-target effects of each siRNA together with the on-target effects as a complex perturbation of many genes and then (2) to extract epistatic interactions from a collection of the complex perturbations using a sparse regression framework. The paper sets up this framework, conducts an extensive simulation study where this approach is implemented using two different existing methods for sparse (elastic net) regression, and provides a reanalysis of an existing siRNA dataset.

The contribution here is sound, and indeed a rather creative idea, and is certainly suitable for PLoSOne with a few minor corrections.

- The definition of marginal epistasis could be improved by defining it in terms of averages rather than a sums. Then the fitness of a genotype in the marginal fitness landscape would simply be the expected fitness of a genotype given its allelic state at the specified subset of genes, and Corollary 1 simplifies to \\epsilon_{i,j}=\\beta_{i,j} instead of \\epsilon_{i,j}=2^{p-2} \\beta_{i,j}.

- The authors should also disambiguate between their concept of marginal epistasis and the (different) marginal epistasis concept studied in e.g. Crawford et al. 2017 PLoS Genetics.

- Sparse regression (Lasso, elastic map, and their Bayesian analogs) has previously been applied both in the fitness landscape and the quantitative genetics literature, and the reader should be aware of how the current contribution fits into this broader context. I am thinking of things like Cai et al. 2011 "Fast empirical Bayesian LASSO for multiple quantitative trait locus mapping" in the quantitative genetics literature and Otwinowski and Nemenman 2013 and Poelwijk et al. 2019 in the fitness landscape literature, but there is additional work in both areas (especially in quantitative genetics). One important feature of L1 regularized regression is that the results are sensitive to the choice of basis, and the Taylor basis used here is one difference with most other treatments of epistasis via regularized regression.

- The re-analysis of the siRNA screen should be clarified. The current text says that the kinases being targeted are kinases of the pathogens, but it appears that these are in fact the kinases of the human host. More generally, the reader needs more background on the experimental design of this study in order to understand the reanalysis. In Table 1, the reader also needs to see the corresponding single mutation effects in order to understand what kind of epistatic interactions these are (e.g. which of these are synthetic lethals), and some of the estimated effects seem awfully large (e.g. 172.04 on a log 2 scale).

Minor additional comments:

- More precise notation would be helpful in Eqn 6

- pg. 6 Karush-Kuhn-Tucker conditions

- pg. 7 \\theta needs to be defined

- pg. 17 What does failed to run mean?This paper describes a technique for inferring epistatic interactions from siRNA perturbation screens. The main ideas are (1) to treat the off-target effects of each siRNA together with the on-target effects as a complex perturbation of many genes and then (2) to extract epistatic interactions from a collection of the complex perturbations using a sparse regression framework. The paper sets up this framework, conducts an extensive simulation study where this approach is implemented using two different existing methods for sparse (elastic net) regression, and provides a reanalysis of an existing siRNA dataset.

The contribution here is sound, and indeed a rather creative idea, and is certainly suitable for PLoSOne with a few minor corrections.

- The definition of marginal epistasis could be improved by defining it in terms of averages rather than a sums. Then the fitness of a genotype in the marginal fitness landscape would simply be the expected fitness of a genotype given its allelic state at the specified subset of genes, and Corollary 1 simplifies to \\epsilon_{i,j}=\\beta_{i,j} instead of \\epsilon_{i,j}=2^{p-2} \\beta_{i,j}.

- The authors should also disambiguate between their concept of marginal epistasis and the (different) marginal epistasis concept studied in e.g. Crawford et al. 2017 PLoS Genetics.

- Sparse regression (Lasso, elastic map, and their Bayesian analogs) has previously been applied both in the fitness landscape and the quantitative genetics literature, and the reader should be aware of how the current contribution fits into this broader context. I am thinking of things like Cai et al. 2011 "Fast empirical Bayesian LASSO for multiple quantitative trait locus mapping" in the quantitative genetics literature and Otwinowski and Nemenman 2013 and Poelwijk et al. 2019 in the fitness landscape literature, but there is additional work in both areas (especially in quantitative genetics). One important feature of L1 regularized regression is that the results are sensitive to the choice of basis, and the Taylor basis used here is one difference with most other treatments of epistasis via regularized regression.

- The re-analysis of the siRNA screen should be clarified. The current text says that the kinases being targeted are kinases of the pathogens, but it appears that these are in fact the kinases of the human host. More generally, the reader needs more background on the experimental design of this study in order to understand the reanalysis. In Table 1, the reader also needs to see the corresponding single mutation effects in order to understand what kind of epistatic interactions these are (e.g. which of these are synthetic lethals), and some of the estimated effects seem awfully large (e.g. 172.04 on a log 2 scale).

Minor additional comments:

- More precise notation would be helpful in Eqn 6

- pg. 6 Karush-Kuhn-Tucker conditions

- pg. 7 \\theta needs to be defined

- pg. 17 What does failed to run mean?

Reviewer #2: I would like to thank the editor for the possibility of reviewing this article entitled “LEARNING EPISTATIC GENE INTERACTIONS FROM PERTURBATION SCREENS” by Elmes et al.

The topic presented is attractive as there is still an unmet need for the understanding of the epistatic gene interactions and its role in developing new therapeutic approach to achieve synthetic lethality.

The abstract summarizes the entire content of the article. The figures and tables help in summarizing its content.

However, this article presents several weaknesses. Here you can find a list of general comments and revisions:

• I have concerns about the two-stage approach to identify interactions. Using linear model to calculate estimates and p values does not take in account the selection process of LASSO. Refitting the regression model after variable selection may lead to biased estimates. However, some advances have been done in this area (Taylor & Tibshirani, 2016; D. Lee, J. et al, 2016).

• Importance of interactions is selected based on p value, however the criteria should be effect size along with p value. Consequently, please, review the following sentence from page 8 “We are nonetheless able (…)” I would suggest to remove it.

• I would like the authors to explain if the impact of the scalability could be a problem in real world data.

• As a general comment, strong emphasis is done to simulated data but there is a need to further investigate the effects of this methodology in real world data.

• The last paragraph of the results section compares R2 of two models, however it is well-known that R2 increases when including more covariates. It is recommended to compare models with a complexity penalization approach, i.e, AIC.

• I would like to know if the authors studied the role of epistatic gene interactions in any specific disease. The presence of some clinical example could improve the quality of the article making it more easily readable.

• As a minor comment, when referring to the Normal distribution in an equation it is conventionally assumed to write it as N(�, �2) instead of Norm(�, �2)

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Reviewer #1: No

Reviewer #2: No

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Learning epistatic gene interactions from perturbation screens

PONE-D-20-30514R1

Dear Dr. Gavryushkin,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Ruben Artero, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: (No Response)

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If you choose “no”, your identity will remain anonymous but your review may still be made public.

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Reviewer #1: No

Reviewer #2: No